Environmental, social, and governance investing and sustainability of pension funds: Evidence from the organisation for economic cooperation and development countries
Bibliographic record
Abstract
Various stakeholders have pressed pension funds (PFs) to refocus their investment strategies on environmental, social and governance (ESG) principles; however, limited attention has been devoted to the impact of sustainable investing on PF assets. Using data from the Organisation for Economic Cooperation and Development (OECD) countries from 1999 to 2022 and using the generalised method of moment estimation, we find that the growth of pension assets in countries with high sustainability scores has slowed since the sustainable development goals were adopted. The impact is more pronounced in the European Union (EU) countries. This finding is noteworthy because EU countries are known for their leadership in sustainable development. Capital market returns are the primary channel through which sustainable investing contributes to reduced pension asset growth. Our findings provide policymakers with important information about the unintended costs of addressing climate risk through exclusionary PF policies. In OECD countries with low fertility rates and ageing populations, the cost exacerbates the sustainability challenges that PFs face. • We study sustainable investing (SI) effects on pension funds (PF). • SI practices have lowered PF assets in countries with high sustainability scores. • The SI effects are more pronounced in the EU than non-EU countries. • Capital market returns are the channel through which SI affects PF assets. • Significant unintended transition costs hurt the growth of PFs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".